As asked in the title of a paper in Trends in Cognitive Sciences, “Does the huamn mnid raed wrods as a wlohe?” Many people find it easy to read words even if their letters are shuffled. Some do not even notice anything wrong with the words. Indeed, as discussed by S. Dehaene in Reading in the Brain, “these abstract similarity effects are so powerful that we experience little difficulty in raednig etnrie sneetnecs in wihch the ltteers of eervy wrod hvae been miexd up, ecxpet for the frsit and the lsat ltteers.”
These effects are not just mere curiosities. They tell of something fundamental about how the human brain works. For example, they tell that the representation used by the brain is invariant to transpositions of letters when it comes to reading tasks. There is probably a good reason why such a representation would be useful, namely, it will be invariant to noise, reshuffling, and even dropping of certain letters in words. If one could find a representation with similar properties, then it is quite plausible that it would work for other sensory modalities as well.
Currently, Hidden Markov Models, or HMMs, are the predominant method for performing recognition tasks, e.g. speech recognition. They are also used in Computational Bioinformatics to do gene prediction in large genomic databases. They have been used successfully for human activity recognition, including American Sign Language recognition, recognizing defensive an offensive maneuvers in football plays with potential military applications when players are mapped to friendly and enemy forces.
It was hoped that by divulging the hidden Markov model approach in all its details in a clear and precise manner, the speech research community at large would adopt what would become known as the “invincible approach” to the automatic recognition of speech. Unfortunately, despite promising work aimed at merging statistical and linguistic knowledge and at generalizing the concept of hidden Markov models to develop still more powerful models, it has been noted that there have not been any alternative that outperforms the HMM approach.
The present application provides one such representation that has much broader applications than HMMs, indeed that can be applied to almost any task that has to deal with sequences, representing sequences, encoding sequences, reproducing sequences, and matching potentially noisy sequences, and that can be applied to a variety of practical tasks in various embodiments of the present invention. These and other advantages of the invention, as well as additional inventive features, will be apparent from the description of the invention provided herein.